https://github.com/dvlab-research/VisionReasoner
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Papers with Code 아카이브
Open Vocabulary Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 14
- 보존된 평가 기록
- 124
Open-vocabulary semantic segmentation models aim to accurately assign a semantic label to each pixel in an image from a set of arbitrary open-vocabulary texts.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/earth-insights/samroadplus
SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images
https://github.com/facebookresearch/dinov2
DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment
https://github.com/ferenas/pgseg
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
https://github.com/hiyouga/easyr1
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/jack-bo1220/awesome-remote-sensing-foundation-models
https://github.com/lchen1019/ERR-Seg
Efficient Redundancy Reduction for Open-Vocabulary Semantic Segmentation
https://github.com/paulcouairon/diffcut
Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning
https://github.com/xmed-lab/clipn
A Closer Look at the Explainability of Contrastive Language-Image Pre-training
https://github.com/xujxyang/opentrans
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.